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Field-based implementation of machine learning forecasting for energy-efficient temperature control in a greenhouse system
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Published: 5 June 2026
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In this study, we developed a simulation model for the internal environment of greenhouses using artificial intelligence techniques. This study focuses on the practical application of machine learning (ML)-based forecasting for greenhouse temperature control and its potential to improve energy efficiency under real-world operating conditions. This study was conducted to optimize energy consumption through smart agriculture applications. A prediction model was established using a time-series ML approach, followed by the development of forecasting models through gap-labeling feature analysis. Data were collected from a greenhouse equipped with a pellet boiler-based heating system over 55 d under real operating conditions. Iterative learning was conducted using 7 d of training data and 1 d of validation data, resulting in 48 models (average r2 = 0.77, RMSE = 1.43). Based on the forecasting results, a data-driven control strategy was applied to adjust the heating operation in advance. The reduction in heating operation time demonstrates improved energy efficiency of the greenhouse system under practical operating conditions by minimizing unnecessary heating. Implementing data-driven predictive control using the developed forecasting model can save 5 %–15% of the energy. Statistical analysis using the Wilcoxon signed-rank test indicated that the performance differences among the models were not statistically significant (p > 0.05). Therefore, this study emphasizes the practical implementation of machine learning-based forecasting for real-time greenhouse control under actual operating conditions.
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CRediT authorship contribution
Sunyong Park, conceptualization, data curation, and writing—original draft; Dae Hyun Kim, methodology development, model implementation, and formal analysis; Kwang Cheol Oh, supervision, research design, validation, project administration, and writing—review and editing. All authors read and approved the final version of the manuscript and agreed to be accountable for all aspects of the work.
Supporting Agencies
This work was supported by the National Research Foundation of Korea [grant number NRF- 2022R1C1C2009821]Data Availability Statement
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
How to Cite
“Field-based implementation of machine learning forecasting for energy-efficient temperature control in a greenhouse system” (2026) Journal of Agricultural Engineering [Preprint]. doi:10.4081/jae.2026.2199.
Copyright (c) 2026 the Author(s)
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